2 citations · 10 across the 17 of their papers we have counts for
20 papers
Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting
Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi
We investigate whether coupling a time-series foundation model to hydraulic project knowledge improves surrogate forecasting of HEC-RAS water-surface elevation (WSE). We present KG…
DU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling
Enrique Hernandez Noguera, Md Meftahul Ferdaus, Nathan Cooper +2
Phase-resolving wave models such as FUNWAVE-TVD are the accuracy standard for nearshore dynamics, resolving the shoaling, refraction, and breaking of individual waves, but their co…
A Browser-Native Digital Test Range for Benchmarking 4D Ocean-Glider Planning Algorithms
Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi
Repeated in-situ evaluation of ocean-glider planners requires scarce vehicles, operators, deployment and recovery resources, and ocean conditions that cannot be reset for competing…
Learned Response-Field Inertia Operator for HEC-RAS 2D Water-Surface Elevation Prediction
Edward Holmberg, Elias Ioup, Md Meftahul Ferdaus +2
This article presents a cross-dataset evaluation of learned native-cell surrogate models for solver-consistent water-surface elevation (WSE) prediction in HEC-RAS 2D. To avoid rast…
Bridging Spectral Operator Learning and U-Net Hierarchies: SpectraNet for Stable Autoregressive PDE Surrogates
Enrique Hernández Noguera, Md Meftahul Ferdaus, Elias Ioup +2
Neural operators for time-dependent PDEs face a structural tension: spectral architectures (FNO and descendants) inherit exponential rollout-error growth from their one-step Lipsch…
DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation
Enrique Hernandez Noguera, Md Meftahul Ferdaus, Elias Ioup +1
Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imbalance, and the need for prec…
VeloxNet: Efficient Spatial Gating for Lightweight Embedded Image Classification
Md Meftahul Ferdaus, Elias Ioup, Mahdi Abdelguerfi +4
Deploying deep learning models on embedded devices for tasks such as aerial disaster monitoring and infrastructure inspection requires architectures that balance accuracy with stri…
A Comprehensive Review of Phase-Averaged and Phase-Resolving Wave Models for Coastal Modeling Applications
Md Meftahul Ferdaus, Nathan Alton Cooper, Austin B. Schmidt +4
Predicting ocean wave behavior is challenging due to the difficulty in choosing suitable numerical models among many with varying capabilities. This review examines the development…
A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics
Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi
The coordination of autonomous agents in dynamic environments is hampered by the semantic gap between high-level mission objectives and low-level planner inputs. To address this, w…
KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning
Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup +3
Semantic segmentation of structural defects in civil infrastructure remains challenging due to variable defect appearances, harsh imaging conditions, and significant class imbalanc…
Few-Shot Learning in Video and 3D Object Detection: A Survey
Md Meftahul Ferdaus, Kendall N. Niles, Joe Tom +2
Few-shot learning (FSL) enables object detection models to recognize novel classes given only a few annotated examples, thereby reducing expensive manual data labeling. This survey…
Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting
Edward Holmberg, Pujan Pokhrel, Maximilian Zoch +9
Physics-based solvers like HEC-RAS provide high-fidelity river forecasts but are too computationally intensive for on-the-fly decision-making during flood events. The central chall…
Physics-Informed Neural Network Surrogate Models for River Stage Prediction
Maximilian Zoch, Edward Holmberg, Pujan Pokhrel +8
This work investigates the feasibility of using Physics-Informed Neural Networks (PINNs) as surrogate models for river stage prediction, aiming to reduce computational cost while m…
Knowledge Graph-Based Multi-Agent Path Planning in Dynamic Environments using WAITR
Ted Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi
This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUV…
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements
Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup +4
We introduce KANICE (Kolmogorov-Arnold Networks with Interactive Convolutional Elements), a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmog…
STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies
Ted Edward Holmberg, Mahdi Abdelguerfi, Elias Ioup
Spatiotemporal networks' observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal…
A Stochastic Geo-spatiotemporal Bipartite Network to Optimize GCOOS Sensor Placement Strategies
Ted Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi
This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observ…
A LightGBM based Forecasting of Dominant Wave Periods in Oceanic Waters
Pujan Pokhrel
In this paper, we propose a Light Gradient Boosting (LightGBM) to forecast dominant wave periods in oceanic waters. First, we use the data collected from CDIP buoys and apply vario…
Random Forest Classifier Based Prediction of Rogue waves on Deep Oceans
Pujan Pokhrel, Elias Ioup, Md Tamjidul Hoque +2
In this paper, we present a novel approach for the prediction of rogue waves in oceans using statistical machine learning methods. Since the ocean is composed of many wave systems,…
Distributed Streaming Analytics on Large-scale Oceanographic Data using Apache Spark
Janak Dahal, Elias Ioup, Shaikh Arifuzzaman +1
Real-world data from diverse domains require real-time scalable analysis. Large-scale data processing frameworks or engines such as Hadoop fall short when results are needed on-the…